Design and Application of Secret Codes for Learning Medical Data
Source abstract
Abstract
In distributed learning for data requiring privacy preservation, such as medical data, the distribution of secret information is an important problem. In this paper, we propose a framework for secret codes in application to distributed systems. Then, we provide new methods to construct such codes using the synthesis or decomposition of previously known minimal codes. The numerical results show that new constructions can generate codes with more flexible parameters than original constructions in the sense of the number of possible weights and the range of weights. Thus, the secret codes from new constructions may be applied to more general situations or environments in distributed systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Coding theory and cryptography · DNA and Biological Computing · Cryptography and Data Security
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical and Computer Engineering, University of Ulsan, Ulsan 44610, Korea
References (25)
Show source-provided references
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